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Probabilistic Programming

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

( Image credit: Michael Betancourt )

Papers

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Showing 26–50 of 273 papers

TitleStatusHype
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
From Probabilistic Programming to Complexity-based Programming—0
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
A Heavy-Tailed Algebra for Probabilistic Programming—0
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Calibration of MEMS AccelerometersCode0
Automating Model Comparison in Factor GraphsCode0
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
String Diagrams with Factorized Densities—0
Dimensionality Reduction as Probabilistic Inference—0
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving—0
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Differentiable Quantum Programming with Unbounded LoopsCode0
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